TuX2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX2 inherits the benefits of an elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs; we extend and optimize it for distributed machine learning to support heterogeneity, a Stale Synchronous Parallel model, and a new MEGA (Mini-batch, Exchange, GlobalSync, and
\nApply) model.
\nWe have developed a set of representative distributed machine learning algorithms in TuX2, covering both supervised and unsupervised learning. Compared to implementations on distributed machine learning platforms, writing these algorithms in TuX2 takes only about 25% of the code: Our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. Our extensive evaluation of TuX2, using large data sets with up to 64 billion edges, shows that TuX2 outperforms state-of-the-art distributed graph engines PowerGraph and PowerLyra by an order of
\nmagnitude, while beating two state-of-the-art distributed machine learning systems by at least 48%.<\/p>\n","protected":false},"excerpt":{"rendered":"
TuX2: Distributed Graph Computation for Machine Learning TuX2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX2 inherits the benefits of an elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs; we extend and optimize it for distributed machine learning to support heterogeneity, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-428202","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX Association","msr_edition":"","msr_affiliation":"","msr_published_date":"2017-03-28","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"669-682","msr_chapter":"","msr_isbn":"978-1-931971-37-9","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/xiao","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/xiao","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"https:\/\/www.usenix.org\/conference\/nsdi17\/technical-sessions\/presentation\/xiao"}],"msr-author-ordering":[{"type":"user_nicename","value":"jxue","user_id":36987,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jxue"},{"type":"user_nicename","value":"yomia","user_id":35038,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=yomia"},{"type":"user_nicename","value":"cncng","user_id":31458,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=cncng"},{"type":"user_nicename","value":"miw","user_id":32960,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=miw"},{"type":"user_nicename","value":"lidongz","user_id":32673,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=lidongz"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[920469,922377,510017],"msr_project":[555282,170955],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":555282,"post_title":"Deep Learning Compiler and Optimizer","post_name":"deep-learning-compiler-and-optimizer","post_type":"msr-project","post_date":"2018-12-04 18:10:52","post_modified":"2023-07-10 03:41:13","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/deep-learning-compiler-and-optimizer\/","post_excerpt":"Project Overview This project aims to build a deep learning compiler and optimizer infrastructure that can provide automatic scalability and efficiency optimization for distributed and local execution.\u00a0 Overall, this stack covers two types of general optimizations: fast distributed training over large-scale servers and efficient local execution on various hardware devices.\u00a0 Currently, our optimizations focus on many different parts of the system stack, such as fast distributed training over RDMA, automatic computation placement across devices, automatic…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/555282"}]}},{"ID":170955,"post_title":"Graph Storage and Analysis","post_name":"temporal-graph-storage-and-analysis-of-social-data","post_type":"msr-project","post_date":"2012-05-17 23:28:59","post_modified":"2020-04-20 22:48:07","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/temporal-graph-storage-and-analysis-of-social-data\/","post_excerpt":"An explosion of user-generated data from online social networks motivates analysis to extract deep insights from this data's graph at scale, even of social, temporal, spatial, and topical connections. 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